Modelling palaeoecological datasets using a state-space approach: two case-studies

Quinn Asena, Jack Williams, and Tony Ives

2025-08-08

People


  • Jack Williams
  • Tony Ives
  • Angie Perotti
  • Nora Schlenker
  • David Nelson
  • Bryan Schuman
  • Jonathan Johnson
  • Vania Stefanova

multinomialTS


A state-space approach to modelling multinomially distributed community data.

  • ESA 2023: presented early development.
  • ESA 2024: ran workshop on how to fit multinomialTS.
  • ESA 2025!: presenting developed use-cases.
  • Do you have multinomially distributed data?
  • I’ll try to leave a couple of extra minutes for discussion.

Beyond pattern recognition


The cutting edge in palaeoecology is to establish potential relationships between observed patterns in species relative abundances and environmental covariates. For example, are observed patterns driven by:

  • species interactions?
  • climate variability?
  • fire regime?
  • …

This is what we want to know if we are to use palaeoecology to inform management of contemporary ecosystems or inform potential future ecosystem states. No easy task!

State-space modelling


State-space modelling goes beyond descriptive approaches and attempts to estimate:

  • autoregressive / density dependent processes
  • interspecific interactions
  • species-environment interactions
  • combinations of the above

multinomialTS

  • models a multinomial distribution (i.e., count data) directly
  • abotic drivers
  • biotic interactions
  • Asena et al., in review

Case studies


Tulane

  • Florida
  • ~60,000 year-long record
  • centennial to millennial dynamics
  • covariates
    • fungal spores (proxy for megaherbivory)
    • \(CO_2\) \(\delta18O\) (climate)
    • Heinrich events (climate-related)
    • charcoal (fire)
  • Williams et al., in prep, Grimm et al. (1993); Grimm et al. (2006)

Sunfish

  • Pennsylvania
  • ~13,000 years
  • decadal to centennial dynamics
  • covariates
    • lake level (proxy for humidity)
  • Johnson et al., in review

Tulane variables

Fitting Tulane

Questions/hypotheses:

  • are biotic interactions or climatic variability the primary drivers of change?
  • is the holocene period significantly different to the full 60,000 years?

Parameter selection

  • window span / prediction resolution 200 years
  • species/taxonomic group selection
    • two functional groups
    • two key dominant species
  • interactions estimated:
    • no interactions
    • pine vs oak

What can we detect, what do we lose?

  • lose: fine-scale processes (e.g., post-fire recovery)

Tulane results

Supported hypotheses: climatic variability over biotic interactions had stronger statistical support (given the data!)

Sunfish description

Fitting Sunfish

Parameter selection

  • window span / prediction resolution 100 years
  • species/taxonomic group selection
    • five most dominant species
    • Eastern white pine, hemlock, beech, oak, and birch
  • interactions:
    • no interactions
    • estimated between pine and hemlock (for this example)

What can we detect, what do we lose?

  • lose: multiple drivers

Sunfish results

Supported Hypotheses

Comparison of results

Results are not causal but test multiple hypotheses

Thank you for listening!

References

Grimm, Eric C., George L. Jacobson, William A. Watts, Barbara C. S. Hansen, and Kirk A. Maasch. 1993. “A 50,000-Year Record of Climate Oscillations from Florida and Its Temporal Correlation with the Heinrich Events.” Science 261 (5118): 198–200. https://doi.org/10.1126/science.261.5118.198.
Grimm, Eric C., William A. Watts, George L. Jacobson, Barbara C. S. Hansen, Heather R. Almquist, and Ann C. Dieffenbacher-Krall. 2006. “Evidence for Warm Wet Heinrich Events in Florida.” Quaternary Science Reviews 25 (17): 2197–2211. https://doi.org/10.1016/j.quascirev.2006.04.008.